Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Cremonesi, Francesco"'
Autor:
Taiello, Riccardo, Cansiz, Sergen, Vesin, Marc, Cremonesi, Francesco, Innocenti, Lucia, Önen, Melek, Lorenzi, Marco
Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. In particular, with respect to the federated aggregation procedure, researchers have been focusing on the study of
Externí odkaz:
http://arxiv.org/abs/2409.00974
Autor:
Innocenti, Lucia, Antonelli, Michela, Cremonesi, Francesco, Sarhan, Kenaan, Granados, Alejandro, Goh, Vicky, Ourselin, Sebastien, Lorenzi, Marco
Healthcare data is often split into medium/small-sized collections across multiple hospitals and access to it is encumbered by privacy regulations. This brings difficulties to use them for the development of machine learning and deep learning models,
Externí odkaz:
http://arxiv.org/abs/2309.17097
Autor:
Cremonesi, Francesco, Vesin, Marc, Cansiz, Sergen, Bouillard, Yannick, Balelli, Irene, Innocenti, Lucia, Silva, Santiago, Ayed, Samy-Safwan, Taiello, Riccardo, Kameni, Laetita, Vidal, Richard, Orlhac, Fanny, Nioche, Christophe, Lapel, Nathan, Houis, Bastien, Modzelewski, Romain, Humbert, Olivier, Önen, Melek, Lorenzi, Marco
The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security. While today several
Externí odkaz:
http://arxiv.org/abs/2304.12012
Autor:
Balelli, Irene, Sportisse, Aude, Cremonesi, Francesco, Mattei, Pierre-Alexandre, Lorenzi, Marco
Federated learning allows for the training of machine learning models on multiple decentralized local datasets without requiring explicit data exchange. However, data pre-processing, including strategies for handling missing data, remains a major bot
Externí odkaz:
http://arxiv.org/abs/2304.08054
Autor:
Cremonesi, Francesco, Schürmann, Felix
Computational modeling and simulation have become essential tools in the quest to better understand the brain's makeup and to decipher the causal interrelations of its components. The breadth of biochemical and biophysical processes and structures in
Externí odkaz:
http://arxiv.org/abs/1906.02757
Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer p
Externí odkaz:
http://arxiv.org/abs/1901.05344
Akademický článek
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Autor:
Álvarez, Federico, Zazo, Santiago, Parras, Juan, Almodóvar, Alejandro, Alonso, Patricia, Giampieri, Enrico, Castellani, Gastone, Sani, Lorenzo, Rollo, Cesare, Sanavia, Tiziana, Krogh, Anders, Prada-Luengo, Íñigo, Kanterakis, Alexandros, Sfakianakis, Stelios, Cremonesi, Francesco
This report comprises the first contributions from different partners on Federated Learning (FL). Aftera preliminary introductory section where the fundamental procedures and limitations are described,we detail the well-known mathematical foundation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17ccfba3e795af609a3a85c56d988eb3
Monitoring data protection compliance for an initiative like GenoMed4All can only besuccessful if there is a swift understanding of compliance across all partners and a clearconcordance. To that end, the need for a reliable and consistent approach to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac829e6fe437875eaa7842cabf317c57
The first article in GenoMed4All's series of Knowledge Pills: an introduction to Machine Learning in healthcare
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::625c4cb666a17599be5da810f5a42b94